Abstract
Controlled-potential in situ thin-layer spectropotentiometry was leveraged to generate visible/near-infrared (VIS/NIR) absorption spectral data sets for the development of chemometric models to quantify Np(III/IV/V/VI) oxidation states in HNO3. This technology would be valuable in laboratory studies and when monitoring process solutions to guide feed adjustments for radiochemical separations—the performance of which depends on oxidation state. This approach successfully isolated and stabilized Np species in pure (∼99%) oxidation states without compromising solution optical properties. Multivariate curve resolution–alternating least squares models were evaluated to resolve spectral and component concentrations from a scan that sequentially produced Np(VI), Np(V), Np(IV), and Np(III) spectra with mixtures of two valences at a time. Although it provided a useful approximation, the method was not able to quantitively resolve each component likely because of rotational ambiguity. Additionally, partial least squares regression models were built from artificial and electrochemically generated VIS/NIR spectral training sets to study the effect of interionic interactions on spectral characteristics. Models built with true Bi-chemical mixtures of coexisting Np oxidation states and spectra generated from additive combinations of pure end points had similar prediction performance. This methodology can be used to directly quantify Np concentration and the ratio of Np oxidation states and other actinides in remote settings such as hot cells.
| Original language | English |
|---|---|
| Article number | 1323372 |
| Journal | Frontiers in Nuclear Engineering |
| Volume | 2 |
| DOIs | |
| State | Published - 2023 |
Funding
The author(s) declare financial support was received for the research, authorship, and/or publication of this article. Funding for this effort was provided by the Science Mission Directorate of NASA and administered by the US Department of Energy Office of Nuclear Energy under contract DEAC05-00OR22725. This work used resources at the Radiochemical Engineering Development Center operated by the US Department of Energy’s Oak Ridge National Laboratory. The authors wish to thank Laetitia Delmau and Kristian Myhre for helpful discussions regarding Np spectrophotometry and electrochemistry. This work was supported by the Pu Supply Program at Oak Ridge National Laboratory. This manuscript has been authored by UT-Battelle LLC under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ).
Keywords
- actinide
- machine learning
- multivariate analysis
- online monitoring
- optical spectroscopy